A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

Abstract

In this paper, the problem of finding an assignment of “n” surgeries to be presented in one of “m” identical operating rooms (ORs) or machines as the surgical case scheduling problem (SCSP) is proposed. Since ORs are among NP-hard optimization problems, mathematical and metaheuristic methods to address OR optimization problems are used. The job or surgical operation ordering in any OR is a permanent part of all sequencing and scheduling problems. The transportation times between ORs are defined based on the type of surgical operations and do not depend on distance, so there is no surgical operation waiting time for transferring. These problems are called no-wait open-shop scheduling problems (NWOSP) with transportation times. The transportation system for the problems is considered a multi-transportation system with no limitation on the number of transportation devices. Accordingly, this study modeled a novel combined no-wait open-shop surgical case scheduling problem (NWOSP-SCSP) with multi-transportation times for the first time to minimize the maximum percentile of makespan for OR as a single objective model. A mixed-integer linear program (MILP) with small-sized instances is solved. In addition to the small-sized model, a novel metaheuristic based on a hybrid simulated annealing (SA) algorithm to solve large-sized problems in an acceptable computational time is suggested, considering the comparison of the SA algorithm and a new recommended heuristic algorithm. Then, the proposed hybrid SA and SA algorithms are compared based on their performance measurement. After reaching the results with a numerical analysis in Nova Scotia health authority hospitals and health centers, the hybrid SA algorithm has generated significantly higher performance than the SA algorithm.

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

“…Many research papers have explored basic DEA models for public hospitals and acute care centers [ 39 , 40 , 41 , 42 , 43 ]. However, in this narrative review, our focus is only on articles applying basic DEA models for EDs, which are discussed below.…”

A Hybrid Discrete Memetic Algorithm for Solving Flow-Shop Scheduling Problems

“…By incorporating stochastic acceptance of worse solutions, simulated annealing is able to escape local optima and has the potential to find near-optimal solutions for complex optimization problems. There are simulated annealing based scheduling algorithms like [25][26][27][28][29][30] Particle swarm optimization (PSO) describes a group of optimization algorithms where the candidate solutions are particles that roam the search space iteratively. There are many PSO variants.…”

Prioritization of Scheduled Surgeries Using Fuzzy Decision Support and Risk Assessment Methods

“…Modern scientific methods are increasingly effective at estimating risk in different areas [12,13]. As an example, the implementation of risk management tools based on a combination of decision-making methods that extract expert knowledge to improve the accuracy of making the best decision was discussed in a paper about implementation in photovoltaic facilities [14]. In fact, expert knowledge and experience play a key role in risk assessment.…”